mcp-openai

<div align="center"> <h1>𝔐&nbsp;&nbsp;mpc-openai&nbsp;&nbsp;✧</h1> <p><em>MCP Client with OpenAI compatible API</em></p> </div> > Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. > > — [https://modelcontextprotocol.io](https://modelcontextprotocol.io) ______________________________________________________________________ > [!WARNING] > This is a simple toy project. Support is not planned. Use as a reference for minimal MCP client development. This is a MCP **client** (not a server). It is meant to be used as a library for building LLMs UI that support MCP through an OpenAI compatible API. This opens the door to locally runnable inference engines ([vLLM](https://github.com/vllm-project/vllm), [Ollama](https://github.com/ollama/ollama), [TGI](https://github.com/huggingface/text-generation-inference), [llama.cpp](https://github.com/ggerganov/llama.cpp), [LMStudio](https://github.com/lmstudio-ai), ...) that support providing support for the OpenAI API ([text generation](https://platform.openai.com/docs/guides/text-generation), [function calling](https://platform.openai.com/docs/guides/function-calling), etc.). ## Usage It is highly recommended to use [uv](https://docs.astral.sh/uv/) in your project based on mpc-openai: - It manages python installation and virtual environment. - It is an executable that can run self-contained python scripts (in our case MCP server) - It is used for CI workflows. Add mcp-openai to your project dependencies with: ``` uv add mcp-openai ``` or use classic pip install. ### Create a MCP client Now you can create a MCP client by specifying your custom configuration. ```python from mcp_openai import MCPClient from mcp_openai import config mcp_client_config = config.MCPClientConfig( mcpServers={ "the-name-of-the-server": config.MCPServerConfig( command="uv", args=["run", "path/to/server/scripts.py/or/github/raw"], ) # add here other servers ... } ) llm_client_config = config.LLMClientConfig( api_key="api-key-for-auth", base_url="https://api.openai.com/v1", ) llm_request_config = config.LLMRequestConfig(model=os.environ["MODEL_NAME"]) client = MCPClient( mcp_client_config, llm_client_config, llm_request_config, ) ``` ### Connect and process messages with MCP client ```python async def main(): # Establish connection between the client and the server. await client.connect_to_server(server_name) # messages_in are coming from user interacting with the LLM # e.g. UI making use of this MCP client. messages_in = ... messages_out = await client.process_messages(messages_in) # messages_out contains the LLM response. If required, the LLM make use of # the available tools offered by the connected servers. ```